Overview

Dataset statistics

Number of variables37
Number of observations2434863
Missing cells18183596
Missing cells (%)20.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory687.3 MiB
Average record size in memory296.0 B

Variable types

Numeric14
Categorical12
Boolean11

Warnings

date_of_report has constant value "20220901.0" Constant
supplementary_prepaid_card has constant value "False" Constant
home_address has a high cardinality: 1807 distinct values High cardinality
credit_card_branch_location has a high cardinality: 120 distinct values High cardinality
current_ca_credit_limit is highly correlated with current_cp_credit_limit and 2 other fieldsHigh correlation
current_cp_credit_limit is highly correlated with current_ca_credit_limit and 2 other fieldsHigh correlation
initial_ca_credit_limit is highly correlated with current_ca_credit_limit and 2 other fieldsHigh correlation
initial_cp_credit_limit is highly correlated with current_ca_credit_limit and 2 other fieldsHigh correlation
current_loan_balance is highly correlated with sanctioned_amountHigh correlation
sanctioned_amount is highly correlated with current_loan_balanceHigh correlation
annual_income is highly correlated with current_cp_credit_limit and 1 other fieldsHigh correlation
current_ca_credit_limit is highly correlated with current_cp_credit_limit and 2 other fieldsHigh correlation
current_cp_credit_limit is highly correlated with annual_income and 3 other fieldsHigh correlation
initial_ca_credit_limit is highly correlated with current_ca_credit_limit and 2 other fieldsHigh correlation
initial_cp_credit_limit is highly correlated with annual_income and 3 other fieldsHigh correlation
current_loan_balance is highly correlated with sanctioned_amountHigh correlation
sanctioned_amount is highly correlated with current_loan_balanceHigh correlation
current_ca_credit_limit is highly correlated with current_cp_credit_limit and 2 other fieldsHigh correlation
current_cp_credit_limit is highly correlated with current_ca_credit_limit and 2 other fieldsHigh correlation
initial_ca_credit_limit is highly correlated with current_ca_credit_limit and 2 other fieldsHigh correlation
initial_cp_credit_limit is highly correlated with current_ca_credit_limit and 2 other fieldsHigh correlation
current_loan_balance is highly correlated with sanctioned_amountHigh correlation
sanctioned_amount is highly correlated with current_loan_balanceHigh correlation
current_loan_balance is highly correlated with sanctioned_amountHigh correlation
opt_out is highly correlated with race and 4 other fieldsHigh correlation
initial_cp_credit_limit is highly correlated with current_cp_credit_limit and 3 other fieldsHigh correlation
prepaid_card_branch_loaction is highly correlated with customer_with_active_prepaid_card and 1 other fieldsHigh correlation
sanctioned_amount is highly correlated with current_loan_balanceHigh correlation
customer_with_active_prepaid_card is highly correlated with prepaid_card_branch_loaction and 1 other fieldsHigh correlation
race is highly correlated with opt_out and 4 other fieldsHigh correlation
marital_status is highly correlated with opt_out and 4 other fieldsHigh correlation
mobile_app_status is highly correlated with prepaid_card_branch_loaction and 2 other fieldsHigh correlation
current_cp_credit_limit is highly correlated with initial_cp_credit_limit and 2 other fieldsHigh correlation
age is highly correlated with opt_out and 4 other fieldsHigh correlation
type_of_employment is highly correlated with occupationHigh correlation
initial_ca_credit_limit is highly correlated with initial_cp_credit_limit and 2 other fieldsHigh correlation
occupation is highly correlated with opt_out and 5 other fieldsHigh correlation
vip_status is highly correlated with initial_cp_credit_limitHigh correlation
customer_with_active_credit_card is highly correlated with mobile_app_statusHigh correlation
current_ca_credit_limit is highly correlated with initial_cp_credit_limit and 2 other fieldsHigh correlation
gender is highly correlated with opt_out and 4 other fieldsHigh correlation
opt_out is highly correlated with educational_qualification and 6 other fieldsHigh correlation
educational_qualification is highly correlated with opt_out and 2 other fieldsHigh correlation
customer_with_active_prepaid_card is highly correlated with mobile_app_status and 2 other fieldsHigh correlation
mobile_app_status is highly correlated with customer_with_active_prepaid_card and 2 other fieldsHigh correlation
own_multiple_financing_products is highly correlated with supplementary_prepaid_card and 1 other fieldsHigh correlation
type_of_employment is highly correlated with occupation and 2 other fieldsHigh correlation
occupation is highly correlated with opt_out and 6 other fieldsHigh correlation
customer_with_active_credit_card is highly correlated with supplementary_prepaid_card and 1 other fieldsHigh correlation
supplementary_credit_card is highly correlated with supplementary_prepaid_card and 1 other fieldsHigh correlation
own_multiple_credit_card is highly correlated with supplementary_prepaid_card and 1 other fieldsHigh correlation
gender is highly correlated with opt_out and 5 other fieldsHigh correlation
residence_type is highly correlated with supplementary_prepaid_card and 1 other fieldsHigh correlation
supplementary_prepaid_card is highly correlated with opt_out and 19 other fieldsHigh correlation
date_of_report is highly correlated with opt_out and 19 other fieldsHigh correlation
race is highly correlated with opt_out and 5 other fieldsHigh correlation
own_multiple_prepaid_card is highly correlated with supplementary_prepaid_card and 1 other fieldsHigh correlation
nationality is highly correlated with supplementary_prepaid_card and 1 other fieldsHigh correlation
marital_status is highly correlated with opt_out and 5 other fieldsHigh correlation
vip_status is highly correlated with supplementary_prepaid_card and 1 other fieldsHigh correlation
customer_with_active_financing_product is highly correlated with supplementary_prepaid_card and 1 other fieldsHigh correlation
default is highly correlated with supplementary_prepaid_card and 1 other fieldsHigh correlation
educational_qualification has 2208750 (90.7%) missing values Missing
current_ca_credit_limit has 2354649 (96.7%) missing values Missing
current_cp_credit_limit has 2354649 (96.7%) missing values Missing
initial_ca_credit_limit has 2361902 (97.0%) missing values Missing
initial_cp_credit_limit has 2361902 (97.0%) missing values Missing
current_prepaid_card_balance has 2095391 (86.1%) missing values Missing
date_of_last_top_up_of_prepaid_card has 2251140 (92.5%) missing values Missing
current_loan_balance has 1092716 (44.9%) missing values Missing
sanctioned_amount has 1092716 (44.9%) missing values Missing
annual_income is highly skewed (γ1 = 1085.445471) Skewed
current_prepaid_card_balance is highly skewed (γ1 = 34.14174152) Skewed
customer_id has unique values Unique
number_of_dependents has 1114516 (45.8%) zeros Zeros
current_prepaid_card_balance has 194365 (8.0%) zeros Zeros
prepaid_card_branch_loaction has 2095391 (86.1%) zeros Zeros
current_loan_balance has 46476 (1.9%) zeros Zeros

Reproduction

Analysis started2023-09-11 15:35:27.604062
Analysis finished2023-09-11 15:45:32.342030
Duration10 minutes and 4.74 seconds
Software versionpandas-profiling v3.0.0
Download configurationconfig.json

Variables

customer_id
Real number (ℝ≥0)

UNIQUE

Distinct2434863
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean161100218.4
Minimum10000019
Maximum970005601
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size18.6 MiB
2023-09-11T15:45:32.493082image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum10000019
5-th percentile10836078.1
Q1110198199.5
median133115686
Q3180718682.5
95-th percentile260619181.4
Maximum970005601
Range960005582
Interquartile range (IQR)70520483

Descriptive statistics

Standard deviation157248499.4
Coefficient of variation (CV)0.9760911624
Kurtosis12.01250985
Mean161100218.4
Median Absolute Deviation (MAD)33115199
Skewness3.354136575
Sum3.92256961 × 1014
Variance2.472709057 × 1016
MonotonicityNot monotonic
2023-09-11T15:45:32.693133image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1429324631
 
< 0.1%
2005109281
 
< 0.1%
1908854951
 
< 0.1%
1300518941
 
< 0.1%
1418748191
 
< 0.1%
7007625201
 
< 0.1%
1302359791
 
< 0.1%
1923251561
 
< 0.1%
1900450211
 
< 0.1%
1327068201
 
< 0.1%
Other values (2434853)2434853
> 99.9%
ValueCountFrequency (%)
100000191
< 0.1%
100000211
< 0.1%
100000241
< 0.1%
100000251
< 0.1%
100000311
< 0.1%
100000351
< 0.1%
100000471
< 0.1%
100000511
< 0.1%
100000581
< 0.1%
100000641
< 0.1%
ValueCountFrequency (%)
9700056011
< 0.1%
9700055301
< 0.1%
9700055181
< 0.1%
9700055061
< 0.1%
9700055041
< 0.1%
9700055011
< 0.1%
9700054971
< 0.1%
9700054861
< 0.1%
9700054691
< 0.1%
9700054651
< 0.1%

date_of_report
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size18.6 MiB
20220901.0
2434863 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters24348630
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row20220901.0
2nd row20220901.0
3rd row20220901.0
4th row20220901.0
5th row20220901.0

Common Values

ValueCountFrequency (%)
20220901.02434863
100.0%

Length

2023-09-11T15:45:33.002746image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2023-09-11T15:45:33.095006image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
20220901.02434863
100.0%

Most occurring characters

ValueCountFrequency (%)
09739452
40.0%
27304589
30.0%
92434863
 
10.0%
12434863
 
10.0%
.2434863
 
10.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number21913767
90.0%
Other Punctuation2434863
 
10.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
09739452
44.4%
27304589
33.3%
92434863
 
11.1%
12434863
 
11.1%
Other Punctuation
ValueCountFrequency (%)
.2434863
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common24348630
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
09739452
40.0%
27304589
30.0%
92434863
 
10.0%
12434863
 
10.0%
.2434863
 
10.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII24348630
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
09739452
40.0%
27304589
30.0%
92434863
 
10.0%
12434863
 
10.0%
.2434863
 
10.0%

gender
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size18.6 MiB
Male
1534446 
Female
900034 
Unknown
 
383

Length

Max length7
Median length4
Mean length4.739761128
Min length4

Characters and Unicode

Total characters11540669
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFemale
2nd rowMale
3rd rowFemale
4th rowFemale
5th rowFemale

Common Values

ValueCountFrequency (%)
Male1534446
63.0%
Female900034
37.0%
Unknown383
 
< 0.1%

Length

2023-09-11T15:45:33.357567image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2023-09-11T15:45:33.464965image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
male1534446
63.0%
female900034
37.0%
unknown383
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
e3334514
28.9%
a2434480
21.1%
l2434480
21.1%
M1534446
13.3%
F900034
 
7.8%
m900034
 
7.8%
n1149
 
< 0.1%
U383
 
< 0.1%
k383
 
< 0.1%
o383
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter9105806
78.9%
Uppercase Letter2434863
 
21.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e3334514
36.6%
a2434480
26.7%
l2434480
26.7%
m900034
 
9.9%
n1149
 
< 0.1%
k383
 
< 0.1%
o383
 
< 0.1%
w383
 
< 0.1%
Uppercase Letter
ValueCountFrequency (%)
M1534446
63.0%
F900034
37.0%
U383
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin11540669
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e3334514
28.9%
a2434480
21.1%
l2434480
21.1%
M1534446
13.3%
F900034
 
7.8%
m900034
 
7.8%
n1149
 
< 0.1%
U383
 
< 0.1%
k383
 
< 0.1%
o383
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII11540669
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e3334514
28.9%
a2434480
21.1%
l2434480
21.1%
M1534446
13.3%
F900034
 
7.8%
m900034
 
7.8%
n1149
 
< 0.1%
U383
 
< 0.1%
k383
 
< 0.1%
o383
 
< 0.1%

occupation
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct31
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size18.6 MiB
a4a8ba27410e227c522ae7b71844852e33ccbb4c57cb362d16e09207de46dda0
393545 
db87f20a1ced8ee129c2e25c6ea20024113c1be0aa50f6a73c9b577d31dddcf3
353778 
0be69302b80664a1f1ada55c8edd5f40cf3d5d23fbb48e47481e05ef097ac67c
251668 
735d33de78e4c7303ea1bbfa00fc0eebc656b24fd8a0d5fecfe8e2da64092701
227929 
cc7cc7b16765e55be80f0b222f9ceea451304704fbc78664597e80a23da9f588
214579 
Other values (26)
993364 

Length

Max length64
Median length64
Mean length64
Min length64

Characters and Unicode

Total characters155831232
Distinct characters16
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1d3134e05d4736dd523e8da18153fee1683082c8047194972a00bef74936fd6d
2nd rowd56baa23c666005144806b1143e76f89d80bb0d5087ac44e63668cd6b415ccc0
3rd row2591e99784666567fe15b2059e3f2050b0e9d1226bde8f911aa4a3e2dcc2436e
4th row3c78d9603676c68c5f26d0f4fb2ed5026456cd799b53aa64373f64b6cda34a43
5th row0be69302b80664a1f1ada55c8edd5f40cf3d5d23fbb48e47481e05ef097ac67c

Common Values

ValueCountFrequency (%)
a4a8ba27410e227c522ae7b71844852e33ccbb4c57cb362d16e09207de46dda0393545
16.2%
db87f20a1ced8ee129c2e25c6ea20024113c1be0aa50f6a73c9b577d31dddcf3353778
14.5%
0be69302b80664a1f1ada55c8edd5f40cf3d5d23fbb48e47481e05ef097ac67c251668
10.3%
735d33de78e4c7303ea1bbfa00fc0eebc656b24fd8a0d5fecfe8e2da64092701227929
9.4%
cc7cc7b16765e55be80f0b222f9ceea451304704fbc78664597e80a23da9f588214579
8.8%
b38c06347169ddbe0700cdba20f63d3e4bbaaacfa6714118d21ea474d70ab2e2211624
8.7%
1d3134e05d4736dd523e8da18153fee1683082c8047194972a00bef74936fd6d133490
 
5.5%
bb83ee9de0d25e9b1a9d509fa87bcd5d4788ae27c16361d453f20536ed202094123122
 
5.1%
3c78d9603676c68c5f26d0f4fb2ed5026456cd799b53aa64373f64b6cda34a43108885
 
4.5%
d56baa23c666005144806b1143e76f89d80bb0d5087ac44e63668cd6b415ccc0105210
 
4.3%
Other values (21)311033
12.8%

Length

2023-09-11T15:45:33.779434image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
a4a8ba27410e227c522ae7b71844852e33ccbb4c57cb362d16e09207de46dda0393545
16.2%
db87f20a1ced8ee129c2e25c6ea20024113c1be0aa50f6a73c9b577d31dddcf3353778
14.5%
0be69302b80664a1f1ada55c8edd5f40cf3d5d23fbb48e47481e05ef097ac67c251668
10.3%
735d33de78e4c7303ea1bbfa00fc0eebc656b24fd8a0d5fecfe8e2da64092701227929
9.4%
cc7cc7b16765e55be80f0b222f9ceea451304704fbc78664597e80a23da9f588214579
8.8%
b38c06347169ddbe0700cdba20f63d3e4bbaaacfa6714118d21ea474d70ab2e2211624
8.7%
1d3134e05d4736dd523e8da18153fee1683082c8047194972a00bef74936fd6d133490
 
5.5%
bb83ee9de0d25e9b1a9d509fa87bcd5d4788ae27c16361d453f20536ed202094123122
 
5.1%
3c78d9603676c68c5f26d0f4fb2ed5026456cd799b53aa64373f64b6cda34a43108885
 
4.5%
d56baa23c666005144806b1143e76f89d80bb0d5087ac44e63668cd6b415ccc0105210
 
4.3%
Other values (21)311033
12.8%

Most occurring characters

ValueCountFrequency (%)
e12355194
 
7.9%
012043342
 
7.7%
d11915480
 
7.6%
210864861
 
7.0%
c10547071
 
6.8%
a10336172
 
6.6%
410312040
 
6.6%
710128844
 
6.5%
b9923593
 
6.4%
69796095
 
6.3%
Other values (6)47608540
30.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number94007069
60.3%
Lowercase Letter61824163
39.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
012043342
12.8%
210864861
11.6%
410312040
11.0%
710128844
10.8%
69796095
10.4%
19334336
9.9%
39265552
9.9%
58589178
9.1%
87853090
8.4%
95819731
6.2%
Lowercase Letter
ValueCountFrequency (%)
e12355194
20.0%
d11915480
19.3%
c10547071
17.1%
a10336172
16.7%
b9923593
16.1%
f6746653
10.9%

Most occurring scripts

ValueCountFrequency (%)
Common94007069
60.3%
Latin61824163
39.7%

Most frequent character per script

Common
ValueCountFrequency (%)
012043342
12.8%
210864861
11.6%
410312040
11.0%
710128844
10.8%
69796095
10.4%
19334336
9.9%
39265552
9.9%
58589178
9.1%
87853090
8.4%
95819731
6.2%
Latin
ValueCountFrequency (%)
e12355194
20.0%
d11915480
19.3%
c10547071
17.1%
a10336172
16.7%
b9923593
16.1%
f6746653
10.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII155831232
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e12355194
 
7.9%
012043342
 
7.7%
d11915480
 
7.6%
210864861
 
7.0%
c10547071
 
6.8%
a10336172
 
6.6%
410312040
 
6.6%
710128844
 
6.5%
b9923593
 
6.4%
69796095
 
6.3%
Other values (6)47608540
30.6%

nationality
Categorical

HIGH CORRELATION

Distinct23
Distinct (%)< 0.1%
Missing384
Missing (%)< 0.1%
Memory size18.6 MiB
522733336a54c81c7cfabcc60dcf26042a4b1d59c4bdf76eafa7c5b79e7e9af6
2434414 
b7a8bf8c2b775d53a0c09b2232326536ba50ef3cf0ee37cc40c7f35b64b99b0a
 
11
2f2599004a972e8ff6cea52d23ee5863ccd75d39c779e30250314038f22edfee
 
8
c8384ec85ceb0443a150112b5410a53daef242f0bc6649e2c4fd7b28454b8fec
 
8
756ccb3836d7412b19620976e8a0e3a3937e935e6b4dc102a780fb4ec3b33e29
 
6
Other values (18)
 
32

Length

Max length64
Median length64
Mean length64
Min length64

Characters and Unicode

Total characters155806656
Distinct characters16
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique11 ?
Unique (%)< 0.1%

Sample

1st row522733336a54c81c7cfabcc60dcf26042a4b1d59c4bdf76eafa7c5b79e7e9af6
2nd row522733336a54c81c7cfabcc60dcf26042a4b1d59c4bdf76eafa7c5b79e7e9af6
3rd row522733336a54c81c7cfabcc60dcf26042a4b1d59c4bdf76eafa7c5b79e7e9af6
4th row522733336a54c81c7cfabcc60dcf26042a4b1d59c4bdf76eafa7c5b79e7e9af6
5th row522733336a54c81c7cfabcc60dcf26042a4b1d59c4bdf76eafa7c5b79e7e9af6

Common Values

ValueCountFrequency (%)
522733336a54c81c7cfabcc60dcf26042a4b1d59c4bdf76eafa7c5b79e7e9af62434414
> 99.9%
b7a8bf8c2b775d53a0c09b2232326536ba50ef3cf0ee37cc40c7f35b64b99b0a11
 
< 0.1%
2f2599004a972e8ff6cea52d23ee5863ccd75d39c779e30250314038f22edfee8
 
< 0.1%
c8384ec85ceb0443a150112b5410a53daef242f0bc6649e2c4fd7b28454b8fec8
 
< 0.1%
756ccb3836d7412b19620976e8a0e3a3937e935e6b4dc102a780fb4ec3b33e296
 
< 0.1%
1e325ce00b0869a7a5dc93dce979cfb355694c25462ee78490aa32f0e1f12c845
 
< 0.1%
4dd44846592eae99c4cb36fe9fb7949a173bdd9e12d3964c3cd3dfc1510bd6e34
 
< 0.1%
6a3fc4dd53c6279fe922c7c5824a4942bbaab9dc5faec66bc8aa3eecf2ad33a43
 
< 0.1%
b005a90e52784e8288643270240a2c7258ee49ea38ad3d8a285926d160b2b9863
 
< 0.1%
d421904b08e73ffb95df8782b428fd213dbd282847a992ac323944a5f7e9c46e2
 
< 0.1%
Other values (13)15
 
< 0.1%
(Missing)384
 
< 0.1%

Length

2023-09-11T15:45:34.085975image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
522733336a54c81c7cfabcc60dcf26042a4b1d59c4bdf76eafa7c5b79e7e9af62434414
> 99.9%
b7a8bf8c2b775d53a0c09b2232326536ba50ef3cf0ee37cc40c7f35b64b99b0a11
 
< 0.1%
2f2599004a972e8ff6cea52d23ee5863ccd75d39c779e30250314038f22edfee8
 
< 0.1%
c8384ec85ceb0443a150112b5410a53daef242f0bc6649e2c4fd7b28454b8fec8
 
< 0.1%
756ccb3836d7412b19620976e8a0e3a3937e935e6b4dc102a780fb4ec3b33e296
 
< 0.1%
1e325ce00b0869a7a5dc93dce979cfb355694c25462ee78490aa32f0e1f12c845
 
< 0.1%
4dd44846592eae99c4cb36fe9fb7949a173bdd9e12d3964c3cd3dfc1510bd6e34
 
< 0.1%
6a3fc4dd53c6279fe922c7c5824a4942bbaab9dc5faec66bc8aa3eecf2ad33a43
 
< 0.1%
b005a90e52784e8288643270240a2c7258ee49ea38ad3d8a285926d160b2b9863
 
< 0.1%
223fc07d653a18ed51a1ed2599d48761068ec78097ab82e5ab9e23dea863314a2
 
< 0.1%
Other values (13)15
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
c19475616
12.5%
714606714
9.4%
a14606714
9.4%
f12172299
 
7.8%
612172282
 
7.8%
29738006
 
6.3%
39737986
 
6.3%
59737926
 
6.3%
49737923
 
6.3%
b9737922
 
6.3%
Other values (6)34083268
21.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number85207093
54.7%
Lowercase Letter70599563
45.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
714606714
17.1%
612172282
14.3%
29738006
11.4%
39737986
11.4%
59737926
11.4%
49737923
11.4%
97303505
8.6%
04869108
 
5.7%
14868996
 
5.7%
82434647
 
2.9%
Lowercase Letter
ValueCountFrequency (%)
c19475616
27.6%
a14606714
20.7%
f12172299
17.2%
b9737922
13.8%
e7303556
 
10.3%
d7303456
 
10.3%

Most occurring scripts

ValueCountFrequency (%)
Common85207093
54.7%
Latin70599563
45.3%

Most frequent character per script

Common
ValueCountFrequency (%)
714606714
17.1%
612172282
14.3%
29738006
11.4%
39737986
11.4%
59737926
11.4%
49737923
11.4%
97303505
8.6%
04869108
 
5.7%
14868996
 
5.7%
82434647
 
2.9%
Latin
ValueCountFrequency (%)
c19475616
27.6%
a14606714
20.7%
f12172299
17.2%
b9737922
13.8%
e7303556
 
10.3%
d7303456
 
10.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII155806656
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
c19475616
12.5%
714606714
9.4%
a14606714
9.4%
f12172299
 
7.8%
612172282
 
7.8%
29738006
 
6.3%
39737986
 
6.3%
59737926
 
6.3%
49737923
 
6.3%
b9737922
 
6.3%
Other values (6)34083268
21.9%

race
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size18.6 MiB
3d0ac26c46f9daaeaff34affc226f2d86300ede16934f3194ef49a5f56c676b5
1929101 
99b0a2098212fca2a4b7e72c48081e4290f2f75004ba54d402df38647113cacc
232116 
83ad5f646c236e5afc36e3f3353c6f6f8ba983653b117d5340f9a5248fe11d00
 
181816
97e44d546f8c88f0a19f0e8db7e2069337b7e6924b7cd230a00710d7b9b53d00
 
91447
b764cdc0eab7137467211272fa539f1260d1bf2e71bcf6ff3bdc960f5c16aa14
 
383

Length

Max length64
Median length64
Mean length64
Min length64

Characters and Unicode

Total characters155831232
Distinct characters16
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3d0ac26c46f9daaeaff34affc226f2d86300ede16934f3194ef49a5f56c676b5
2nd row3d0ac26c46f9daaeaff34affc226f2d86300ede16934f3194ef49a5f56c676b5
3rd row3d0ac26c46f9daaeaff34affc226f2d86300ede16934f3194ef49a5f56c676b5
4th row83ad5f646c236e5afc36e3f3353c6f6f8ba983653b117d5340f9a5248fe11d00
5th row3d0ac26c46f9daaeaff34affc226f2d86300ede16934f3194ef49a5f56c676b5

Common Values

ValueCountFrequency (%)
3d0ac26c46f9daaeaff34affc226f2d86300ede16934f3194ef49a5f56c676b51929101
79.2%
99b0a2098212fca2a4b7e72c48081e4290f2f75004ba54d402df38647113cacc232116
 
9.5%
83ad5f646c236e5afc36e3f3353c6f6f8ba983653b117d5340f9a5248fe11d00181816
 
7.5%
97e44d546f8c88f0a19f0e8db7e2069337b7e6924b7cd230a00710d7b9b53d0091447
 
3.8%
b764cdc0eab7137467211272fa539f1260d1bf2e71bcf6ff3bdc960f5c16aa14383
 
< 0.1%

Length

2023-09-11T15:45:34.401494image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2023-09-11T15:45:34.501941image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
3d0ac26c46f9daaeaff34affc226f2d86300ede16934f3194ef49a5f56c676b51929101
79.2%
99b0a2098212fca2a4b7e72c48081e4290f2f75004ba54d402df38647113cacc232116
 
9.5%
83ad5f646c236e5afc36e3f3353c6f6f8ba983653b117d5340f9a5248fe11d00181816
 
7.5%
97e44d546f8c88f0a19f0e8db7e2069337b7e6924b7cd230a00710d7b9b53d0091447
 
3.8%
b764cdc0eab7137467211272fa539f1260d1bf2e71bcf6ff3bdc960f5c16aa14383
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
f19840107
12.7%
617214275
11.0%
a13646876
8.8%
312294834
 
7.9%
412182702
 
7.8%
210213220
 
6.6%
c9607241
 
6.2%
99466501
 
6.1%
d9184468
 
5.9%
e9092638
 
5.8%
Other values (6)33088370
21.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number91011671
58.4%
Lowercase Letter64819561
41.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
617214275
18.9%
312294834
13.5%
412182702
13.4%
210213220
11.2%
99466501
10.4%
08781735
9.6%
57526091
8.3%
15699888
 
6.3%
83950617
 
4.3%
73681808
 
4.0%
Lowercase Letter
ValueCountFrequency (%)
f19840107
30.6%
a13646876
21.1%
c9607241
14.8%
d9184468
14.2%
e9092638
14.0%
b3448231
 
5.3%

Most occurring scripts

ValueCountFrequency (%)
Common91011671
58.4%
Latin64819561
41.6%

Most frequent character per script

Common
ValueCountFrequency (%)
617214275
18.9%
312294834
13.5%
412182702
13.4%
210213220
11.2%
99466501
10.4%
08781735
9.6%
57526091
8.3%
15699888
 
6.3%
83950617
 
4.3%
73681808
 
4.0%
Latin
ValueCountFrequency (%)
f19840107
30.6%
a13646876
21.1%
c9607241
14.8%
d9184468
14.2%
e9092638
14.0%
b3448231
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII155831232
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
f19840107
12.7%
617214275
11.0%
a13646876
8.8%
312294834
 
7.9%
412182702
 
7.8%
210213220
 
6.6%
c9607241
 
6.2%
99466501
 
6.1%
d9184468
 
5.9%
e9092638
 
5.8%
Other values (6)33088370
21.2%

marital_status
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct5
Distinct (%)< 0.1%
Missing32
Missing (%)< 0.1%
Memory size18.6 MiB
8623744e832ac4d2479a3de6c827dfb779ddb769de07f0a0b2567f27b6cb45f5
1500428 
8316f8178707dee9ea8c0e44178b4993a37244112fd60a0be23dae005a3dca01
887912 
10a033a3053ce4de82085eef58d58d9ed93638cb095d4c6747e992305aebdcbf
 
29083
14a1e8ee89b963b94242cbb2fbfbb1327eb20846632856b18f0562d2218d7371
 
17025
b764cdc0eab7137467211272fa539f1260d1bf2e71bcf6ff3bdc960f5c16aa14
 
383

Length

Max length64
Median length64
Mean length64
Min length64

Characters and Unicode

Total characters155829184
Distinct characters16
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row8623744e832ac4d2479a3de6c827dfb779ddb769de07f0a0b2567f27b6cb45f5
2nd row8316f8178707dee9ea8c0e44178b4993a37244112fd60a0be23dae005a3dca01
3rd row8316f8178707dee9ea8c0e44178b4993a37244112fd60a0be23dae005a3dca01
4th row8623744e832ac4d2479a3de6c827dfb779ddb769de07f0a0b2567f27b6cb45f5
5th row8623744e832ac4d2479a3de6c827dfb779ddb769de07f0a0b2567f27b6cb45f5

Common Values

ValueCountFrequency (%)
8623744e832ac4d2479a3de6c827dfb779ddb769de07f0a0b2567f27b6cb45f51500428
61.6%
8316f8178707dee9ea8c0e44178b4993a37244112fd60a0be23dae005a3dca01887912
36.5%
10a033a3053ce4de82085eef58d58d9ed93638cb095d4c6747e992305aebdcbf29083
 
1.2%
14a1e8ee89b963b94242cbb2fbfbb1327eb20846632856b18f0562d2218d737117025
 
0.7%
b764cdc0eab7137467211272fa539f1260d1bf2e71bcf6ff3bdc960f5c16aa14383
 
< 0.1%
(Missing)32
 
< 0.1%

Length

2023-09-11T15:45:35.246029image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2023-09-11T15:45:35.346118image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
8623744e832ac4d2479a3de6c827dfb779ddb769de07f0a0b2567f27b6cb45f51500428
61.6%
8316f8178707dee9ea8c0e44178b4993a37244112fd60a0be23dae005a3dca01887912
36.5%
10a033a3053ce4de82085eef58d58d9ed93638cb095d4c6747e992305aebdcbf29083
 
1.2%
14a1e8ee89b963b94242cbb2fbfbb1327eb20846632856b18f0562d2218d737117025
 
0.7%
b764cdc0eab7137467211272fa539f1260d1bf2e71bcf6ff3bdc960f5c16aa14383
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
718054951
11.6%
d12763913
 
8.2%
412098198
 
7.8%
211879610
 
7.6%
010926365
 
7.0%
e10101203
 
6.5%
a9934562
 
6.4%
b9520353
 
6.1%
69423553
 
6.0%
39213674
 
5.9%
Other values (6)41912802
26.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number99207315
63.7%
Lowercase Letter56621869
36.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
718054951
18.2%
412098198
12.2%
211879610
12.0%
010926365
11.0%
69423553
9.5%
39213674
9.3%
89188409
9.3%
97362276
7.4%
55598510
 
5.6%
15461769
 
5.5%
Lowercase Letter
ValueCountFrequency (%)
d12763913
22.5%
e10101203
17.8%
a9934562
17.5%
b9520353
16.8%
f7889458
13.9%
c6412380
11.3%

Most occurring scripts

ValueCountFrequency (%)
Common99207315
63.7%
Latin56621869
36.3%

Most frequent character per script

Common
ValueCountFrequency (%)
718054951
18.2%
412098198
12.2%
211879610
12.0%
010926365
11.0%
69423553
9.5%
39213674
9.3%
89188409
9.3%
97362276
7.4%
55598510
 
5.6%
15461769
 
5.5%
Latin
ValueCountFrequency (%)
d12763913
22.5%
e10101203
17.8%
a9934562
17.5%
b9520353
16.8%
f7889458
13.9%
c6412380
11.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII155829184
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
718054951
11.6%
d12763913
 
8.2%
412098198
 
7.8%
211879610
 
7.6%
010926365
 
7.0%
e10101203
 
6.5%
a9934562
 
6.4%
b9520353
 
6.1%
69423553
 
6.0%
39213674
 
5.9%
Other values (6)41912802
26.9%

age
Real number (ℝ≥0)

HIGH CORRELATION

Distinct80
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38.31132552
Minimum0
Maximum122
Zeros383
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size18.6 MiB
2023-09-11T15:45:35.545727image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile24
Q130
median36
Q345
95-th percentile59
Maximum122
Range122
Interquartile range (IQR)15

Descriptive statistics

Standard deviation10.67125772
Coefficient of variation (CV)0.278540551
Kurtosis0.04305776584
Mean38.31132552
Median Absolute Deviation (MAD)7
Skewness0.685594358
Sum93282829
Variance113.8757414
MonotonicityNot monotonic
2023-09-11T15:45:35.749586image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3498275
 
4.0%
3298233
 
4.0%
3597846
 
4.0%
3197794
 
4.0%
3097214
 
4.0%
3697174
 
4.0%
3394371
 
3.9%
2993020
 
3.8%
3792982
 
3.8%
3886804
 
3.6%
Other values (70)1481150
60.8%
ValueCountFrequency (%)
0383
 
< 0.1%
92
 
< 0.1%
131
 
< 0.1%
151
 
< 0.1%
172
 
< 0.1%
18224
 
< 0.1%
194203
 
0.2%
2011633
 
0.5%
2119417
0.8%
2230790
1.3%
ValueCountFrequency (%)
12247
< 0.1%
1152
 
< 0.1%
1121
 
< 0.1%
1021
 
< 0.1%
881
 
< 0.1%
872
 
< 0.1%
863
 
< 0.1%
851
 
< 0.1%
849
 
< 0.1%
8313
 
< 0.1%

home_address
Categorical

HIGH CARDINALITY

Distinct1807
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size18.6 MiB
43000
 
33423
81700
 
31048
81200
 
30877
81100
 
30570
81300
 
30421
Other values (1802)
2278524 

Length

Max length5
Median length5
Mean length4.994149979
Min length1

Characters and Unicode

Total characters12160071
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique224 ?
Unique (%)< 0.1%

Sample

1st row55100
2nd row84900
3rd row07000
4th row58000
5th row43500

Common Values

ValueCountFrequency (%)
4300033423
 
1.4%
8170031048
 
1.3%
8120030877
 
1.3%
8110030570
 
1.3%
8130030421
 
1.2%
6810027322
 
1.1%
9305025437
 
1.0%
0800024315
 
1.0%
9800024206
 
1.0%
8845023725
 
1.0%
Other values (1797)2153519
88.4%

Length

2023-09-11T15:45:36.150079image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
4300033423
 
1.4%
8170031048
 
1.3%
8120030877
 
1.3%
8110030570
 
1.3%
8130030421
 
1.2%
6810027322
 
1.1%
9305025437
 
1.0%
0800024315
 
1.0%
9800024206
 
1.0%
8845023725
 
1.0%
Other values (1797)2153519
88.4%

Most occurring characters

ValueCountFrequency (%)
05321805
43.8%
11171503
 
9.6%
8885237
 
7.3%
4880556
 
7.2%
5797640
 
6.6%
2793760
 
6.5%
3759225
 
6.2%
7590974
 
4.9%
6516751
 
4.2%
9439059
 
3.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number12156510
> 99.9%
Dash Punctuation3561
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
05321805
43.8%
11171503
 
9.6%
8885237
 
7.3%
4880556
 
7.2%
5797640
 
6.6%
2793760
 
6.5%
3759225
 
6.2%
7590974
 
4.9%
6516751
 
4.3%
9439059
 
3.6%
Dash Punctuation
ValueCountFrequency (%)
-3561
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common12160071
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
05321805
43.8%
11171503
 
9.6%
8885237
 
7.3%
4880556
 
7.2%
5797640
 
6.6%
2793760
 
6.5%
3759225
 
6.2%
7590974
 
4.9%
6516751
 
4.2%
9439059
 
3.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII12160071
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
05321805
43.8%
11171503
 
9.6%
8885237
 
7.3%
4880556
 
7.2%
5797640
 
6.6%
2793760
 
6.5%
3759225
 
6.2%
7590974
 
4.9%
6516751
 
4.2%
9439059
 
3.6%

residence_type
Categorical

HIGH CORRELATION

Distinct7
Distinct (%)< 0.1%
Missing1842
Missing (%)0.1%
Memory size18.6 MiB
c81f5b09f595dd05e9ad3b0fa206caf8a92a2f0f154d2885451ddf7f09768e6c
784974 
98c723844f9e6aaaa5b2361761bdf8197e0ae4c087c688a3212248e0d40ef4e6
735796 
3f61f7d37f20290d7d17f55ce5656469c8d894046147962773153d5bdffc5872
449127 
055a952c818647957a80fc28e0537a01d3ad8e909145b4945f40fc6b508b5506
207907 
a3ccf1236ee8a45f9dd05b9db992f701d0aba9006879742d48ca2f10191bee90
165195 
Other values (2)
90022 

Length

Max length64
Median length64
Mean length64
Min length64

Characters and Unicode

Total characters155713344
Distinct characters16
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row055a952c818647957a80fc28e0537a01d3ad8e909145b4945f40fc6b508b5506
2nd row98c723844f9e6aaaa5b2361761bdf8197e0ae4c087c688a3212248e0d40ef4e6
3rd row98c723844f9e6aaaa5b2361761bdf8197e0ae4c087c688a3212248e0d40ef4e6
4th rowc81f5b09f595dd05e9ad3b0fa206caf8a92a2f0f154d2885451ddf7f09768e6c
5th rowfcaa4020673f3cadd8a84f92af02c59f2b586ea2830d6a078961513287c98dbe

Common Values

ValueCountFrequency (%)
c81f5b09f595dd05e9ad3b0fa206caf8a92a2f0f154d2885451ddf7f09768e6c784974
32.2%
98c723844f9e6aaaa5b2361761bdf8197e0ae4c087c688a3212248e0d40ef4e6735796
30.2%
3f61f7d37f20290d7d17f55ce5656469c8d894046147962773153d5bdffc5872449127
18.4%
055a952c818647957a80fc28e0537a01d3ad8e909145b4945f40fc6b508b5506207907
 
8.5%
a3ccf1236ee8a45f9dd05b9db992f701d0aba9006879742d48ca2f10191bee90165195
 
6.8%
fcaa4020673f3cadd8a84f92af02c59f2b586ea2830d6a078961513287c98dbe80307
 
3.3%
ebdad9ba4c5e3ce0078ad8e8fb31e4c76176834cd2370f05bd043e4baf8d6c5a9715
 
0.4%
(Missing)1842
 
0.1%

Length

2023-09-11T15:45:36.483977image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2023-09-11T15:45:36.588745image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
c81f5b09f595dd05e9ad3b0fa206caf8a92a2f0f154d2885451ddf7f09768e6c784974
32.3%
98c723844f9e6aaaa5b2361761bdf8197e0ae4c087c688a3212248e0d40ef4e6735796
30.2%
3f61f7d37f20290d7d17f55ce5656469c8d894046147962773153d5bdffc5872449127
18.5%
055a952c818647957a80fc28e0537a01d3ad8e909145b4945f40fc6b508b5506207907
 
8.5%
a3ccf1236ee8a45f9dd05b9db992f701d0aba9006879742d48ca2f10191bee90165195
 
6.8%
fcaa4020673f3cadd8a84f92af02c59f2b586ea2830d6a078961513287c98dbe80307
 
3.3%
ebdad9ba4c5e3ce0078ad8e8fb31e4c76176834cd2370f05bd043e4baf8d6c5a9715
 
0.4%

Most occurring characters

ValueCountFrequency (%)
f12897123
 
8.3%
812776574
 
8.2%
012270140
 
7.9%
512054029
 
7.7%
910620804
 
6.8%
a10607973
 
6.8%
d10497505
 
6.7%
210183535
 
6.5%
610033148
 
6.4%
49525541
 
6.1%
Other values (6)44246972
28.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number101598237
65.2%
Lowercase Letter54115107
34.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
812776574
12.6%
012270140
12.1%
512054029
11.9%
910620804
10.5%
210183535
10.0%
610033148
9.9%
49525541
9.4%
79505235
9.4%
18724354
8.6%
35904877
5.8%
Lowercase Letter
ValueCountFrequency (%)
f12897123
23.8%
a10607973
19.6%
d10497505
19.4%
e7729349
14.3%
c7398800
13.7%
b4984357
 
9.2%

Most occurring scripts

ValueCountFrequency (%)
Common101598237
65.2%
Latin54115107
34.8%

Most frequent character per script

Common
ValueCountFrequency (%)
812776574
12.6%
012270140
12.1%
512054029
11.9%
910620804
10.5%
210183535
10.0%
610033148
9.9%
49525541
9.4%
79505235
9.4%
18724354
8.6%
35904877
5.8%
Latin
ValueCountFrequency (%)
f12897123
23.8%
a10607973
19.6%
d10497505
19.4%
e7729349
14.3%
c7398800
13.7%
b4984357
 
9.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII155713344
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
f12897123
 
8.3%
812776574
 
8.2%
012270140
 
7.9%
512054029
 
7.7%
910620804
 
6.8%
a10607973
 
6.8%
d10497505
 
6.7%
210183535
 
6.5%
610033148
 
6.4%
49525541
 
6.1%
Other values (6)44246972
28.4%

educational_qualification
Categorical

HIGH CORRELATION
MISSING

Distinct4
Distinct (%)< 0.1%
Missing2208750
Missing (%)90.7%
Memory size18.6 MiB
9369fec11043557b808b5faf7684d5281b8e69bdb2cf661546a51406e8b89c8d
132195 
74a30f9c0c4638c759eeb1abc1b8857dfa8b26b6dbab306a1e6a3245dbec6235
53222 
dd11755948898b60031cd1382b72e61ca672b0d255e238748a4d9961ead717e8
37013 
30b3ef70d36d51469cd2c93dedfdf377f2cff6caa760d6361f1ab91a29f5070c
 
3683

Length

Max length64
Median length64
Mean length64
Min length64

Characters and Unicode

Total characters14471232
Distinct characters16
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowdd11755948898b60031cd1382b72e61ca672b0d255e238748a4d9961ead717e8
2nd row74a30f9c0c4638c759eeb1abc1b8857dfa8b26b6dbab306a1e6a3245dbec6235
3rd rowdd11755948898b60031cd1382b72e61ca672b0d255e238748a4d9961ead717e8
4th row9369fec11043557b808b5faf7684d5281b8e69bdb2cf661546a51406e8b89c8d
5th row9369fec11043557b808b5faf7684d5281b8e69bdb2cf661546a51406e8b89c8d

Common Values

ValueCountFrequency (%)
9369fec11043557b808b5faf7684d5281b8e69bdb2cf661546a51406e8b89c8d132195
 
5.4%
74a30f9c0c4638c759eeb1abc1b8857dfa8b26b6dbab306a1e6a3245dbec623553222
 
2.2%
dd11755948898b60031cd1382b72e61ca672b0d255e238748a4d9961ead717e837013
 
1.5%
30b3ef70d36d51469cd2c93dedfdf377f2cff6caa760d6361f1ab91a29f5070c3683
 
0.2%
(Missing)2208750
90.7%

Length

2023-09-11T15:45:36.969332image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2023-09-11T15:45:37.065292image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
9369fec11043557b808b5faf7684d5281b8e69bdb2cf661546a51406e8b89c8d132195
58.5%
74a30f9c0c4638c759eeb1abc1b8857dfa8b26b6dbab306a1e6a3245dbec623553222
23.5%
dd11755948898b60031cd1382b72e61ca672b0d255e238748a4d9961ead717e837013
 
16.4%
30b3ef70d36d51469cd2c93dedfdf377f2cff6caa760d6361f1ab91a29f5070c3683
 
1.6%

Most occurring characters

ValueCountFrequency (%)
81529539
 
10.6%
61414847
 
9.8%
b1337351
 
9.2%
51161476
 
8.0%
11094464
 
7.6%
d804110
 
5.6%
4803168
 
5.6%
9798008
 
5.5%
e764891
 
5.3%
c755136
 
5.2%
Other values (6)4008242
27.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number9435563
65.2%
Lowercase Letter5035669
34.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
81529539
16.2%
61414847
15.0%
51161476
12.3%
11094464
11.6%
4803168
8.5%
9798008
8.5%
0685705
7.3%
7664549
7.0%
3663637
7.0%
2620170
6.6%
Lowercase Letter
ValueCountFrequency (%)
b1337351
26.6%
d804110
16.0%
e764891
15.2%
c755136
15.0%
a709493
14.1%
f664688
13.2%

Most occurring scripts

ValueCountFrequency (%)
Common9435563
65.2%
Latin5035669
34.8%

Most frequent character per script

Common
ValueCountFrequency (%)
81529539
16.2%
61414847
15.0%
51161476
12.3%
11094464
11.6%
4803168
8.5%
9798008
8.5%
0685705
7.3%
7664549
7.0%
3663637
7.0%
2620170
6.6%
Latin
ValueCountFrequency (%)
b1337351
26.6%
d804110
16.0%
e764891
15.2%
c755136
15.0%
a709493
14.1%
f664688
13.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII14471232
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
81529539
 
10.6%
61414847
 
9.8%
b1337351
 
9.2%
51161476
 
8.0%
11094464
 
7.6%
d804110
 
5.6%
4803168
 
5.6%
9798008
 
5.5%
e764891
 
5.3%
c755136
 
5.2%
Other values (6)4008242
27.7%

type_of_employment
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing383
Missing (%)< 0.1%
Memory size18.6 MiB
9fb72472a1bfadde532422630d0a0a2531491064f8e9c2e6cc706487159c9920
2342330 
32f074477f7f9efed2b283e51bf8ae7c641d01ee446f44ab30b609bc916d59c2
 
92150

Length

Max length64
Median length64
Mean length64
Min length64

Characters and Unicode

Total characters155806720
Distinct characters16
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row9fb72472a1bfadde532422630d0a0a2531491064f8e9c2e6cc706487159c9920
2nd row9fb72472a1bfadde532422630d0a0a2531491064f8e9c2e6cc706487159c9920
3rd row9fb72472a1bfadde532422630d0a0a2531491064f8e9c2e6cc706487159c9920
4th row9fb72472a1bfadde532422630d0a0a2531491064f8e9c2e6cc706487159c9920
5th row9fb72472a1bfadde532422630d0a0a2531491064f8e9c2e6cc706487159c9920

Common Values

ValueCountFrequency (%)
9fb72472a1bfadde532422630d0a0a2531491064f8e9c2e6cc706487159c99202342330
96.2%
32f074477f7f9efed2b283e51bf8ae7c641d01ee446f44ab30b609bc916d59c292150
 
3.8%
(Missing)383
 
< 0.1%

Length

2023-09-11T15:45:37.364453image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2023-09-11T15:45:37.458931image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
9fb72472a1bfadde532422630d0a0a2531491064f8e9c2e6cc706487159c99202342330
96.2%
32f074477f7f9efed2b283e51bf8ae7c641d01ee446f44ab30b609bc916d59c292150
 
3.8%

Most occurring characters

ValueCountFrequency (%)
219107240
12.3%
914422580
 
9.3%
014422580
 
9.3%
412356700
 
7.9%
79830070
 
6.3%
19737920
 
6.2%
69737920
 
6.2%
c9645770
 
6.2%
a9553620
 
6.1%
f7579890
 
4.9%
Other values (6)39412430
25.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number108998700
70.0%
Lowercase Letter46808020
30.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
219107240
17.5%
914422580
13.2%
014422580
13.2%
412356700
11.3%
79830070
9.0%
19737920
8.9%
69737920
8.9%
37303440
 
6.7%
57211290
 
6.6%
84868960
 
4.5%
Lowercase Letter
ValueCountFrequency (%)
c9645770
20.6%
a9553620
20.4%
f7579890
16.2%
e7579890
16.2%
d7303440
15.6%
b5145410
11.0%

Most occurring scripts

ValueCountFrequency (%)
Common108998700
70.0%
Latin46808020
30.0%

Most frequent character per script

Common
ValueCountFrequency (%)
219107240
17.5%
914422580
13.2%
014422580
13.2%
412356700
11.3%
79830070
9.0%
19737920
8.9%
69737920
8.9%
37303440
 
6.7%
57211290
 
6.6%
84868960
 
4.5%
Latin
ValueCountFrequency (%)
c9645770
20.6%
a9553620
20.4%
f7579890
16.2%
e7579890
16.2%
d7303440
15.6%
b5145410
11.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII155806720
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
219107240
12.3%
914422580
 
9.3%
014422580
 
9.3%
412356700
 
7.9%
79830070
 
6.3%
19737920
 
6.2%
69737920
 
6.2%
c9645770
 
6.2%
a9553620
 
6.1%
f7579890
 
4.9%
Other values (6)39412430
25.3%

annual_income
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED

Distinct407779
Distinct (%)16.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33501.46975
Minimum0
Maximum1000000000
Zeros21360
Zeros (%)0.9%
Negative0
Negative (%)0.0%
Memory size18.6 MiB
2023-09-11T15:45:37.608414image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile11040
Q117292
median25212
Q338736
95-th percentile75002.34
Maximum1000000000
Range1000000000
Interquartile range (IQR)21444

Descriptive statistics

Standard deviation911356.57
Coefficient of variation (CV)27.20348023
Kurtosis1190561.509
Mean33501.46975
Median Absolute Deviation (MAD)9737.4
Skewness1085.445471
Sum8.157148913 × 1010
Variance8.305707978 × 1011
MonotonicityNot monotonic
2023-09-11T15:45:37.809067image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12000109087
 
4.5%
1800051459
 
2.1%
1440045273
 
1.9%
2400034373
 
1.4%
2160026238
 
1.1%
3000024404
 
1.0%
1560022087
 
0.9%
1320021827
 
0.9%
3600021408
 
0.9%
021360
 
0.9%
Other values (407769)2057347
84.5%
ValueCountFrequency (%)
021360
0.9%
0.122
 
< 0.1%
0.481
 
< 0.1%
0.9666
 
< 0.1%
12
 
< 0.1%
25
 
< 0.1%
4.921
 
< 0.1%
92
 
< 0.1%
122407
 
0.1%
12.961
 
< 0.1%
ValueCountFrequency (%)
10000000002
< 0.1%
668321041
< 0.1%
527685001
< 0.1%
372000001
< 0.1%
345846241
< 0.1%
299427241
< 0.1%
282886921
< 0.1%
281520601
< 0.1%
258913441
< 0.1%
249600001
< 0.1%

vip_status
Boolean

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
False
2434813 
True
 
50
ValueCountFrequency (%)
False2434813
> 99.9%
True50
 
< 0.1%
2023-09-11T15:45:37.937684image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

number_of_dependents
Real number (ℝ)

ZEROS

Distinct58
Distinct (%)< 0.1%
Missing383
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean1.566322171
Minimum-1
Maximum83
Zeros1114516
Zeros (%)45.8%
Negative32
Negative (%)< 0.1%
Memory size18.6 MiB
2023-09-11T15:45:38.060871image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile0
Q10
median1
Q33
95-th percentile5
Maximum83
Range84
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.963035124
Coefficient of variation (CV)1.253276727
Kurtosis11.47532085
Mean1.566322171
Median Absolute Deviation (MAD)1
Skewness1.673430899
Sum3813180
Variance3.853506899
MonotonicityNot monotonic
2023-09-11T15:45:38.245646image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01114516
45.8%
1347207
 
14.3%
2318779
 
13.1%
3231967
 
9.5%
4179798
 
7.4%
5136147
 
5.6%
657190
 
2.3%
725839
 
1.1%
812715
 
0.5%
94063
 
0.2%
Other values (48)6259
 
0.3%
ValueCountFrequency (%)
-132
 
< 0.1%
01114516
45.8%
1347207
 
14.3%
2318779
 
13.1%
3231967
 
9.5%
4179798
 
7.4%
5136147
 
5.6%
657190
 
2.3%
725839
 
1.1%
812715
 
0.5%
ValueCountFrequency (%)
831
< 0.1%
821
< 0.1%
811
< 0.1%
611
< 0.1%
602
< 0.1%
591
< 0.1%
581
< 0.1%
571
< 0.1%
541
< 0.1%
522
< 0.1%

mobile_app_status
Boolean

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
False
2281804 
True
 
153059
ValueCountFrequency (%)
False2281804
93.7%
True153059
 
6.3%
2023-09-11T15:45:38.366976image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

opt_out
Boolean

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
False
2434477 
True
 
386
ValueCountFrequency (%)
False2434477
> 99.9%
True386
 
< 0.1%
2023-09-11T15:45:38.421197image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

customer_with_active_credit_card
Boolean

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
False
2354649 
True
 
80214
ValueCountFrequency (%)
False2354649
96.7%
True80214
 
3.3%
2023-09-11T15:45:38.474197image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

current_ca_credit_limit
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct225
Distinct (%)0.3%
Missing2354649
Missing (%)96.7%
Infinite0
Infinite (%)0.0%
Mean3242.249857
Minimum0
Maximum80000
Zeros198
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size18.6 MiB
2023-09-11T15:45:38.597747image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile800
Q11500
median2400
Q34000
95-th percentile8000
Maximum80000
Range80000
Interquartile range (IQR)2500

Descriptive statistics

Standard deviation2946.655532
Coefficient of variation (CV)0.9088304918
Kurtosis29.76281661
Mean3242.249857
Median Absolute Deviation (MAD)1200
Skewness3.781233446
Sum260073830
Variance8682778.822
MonotonicityNot monotonic
2023-09-11T15:45:38.793752image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12008207
 
0.3%
40008147
 
0.3%
24007838
 
0.3%
16006777
 
0.3%
32005137
 
0.2%
15004558
 
0.2%
8004547
 
0.2%
48004502
 
0.2%
20004379
 
0.2%
18002684
 
0.1%
Other values (215)23438
 
1.0%
(Missing)2354649
96.7%
ValueCountFrequency (%)
0198
 
< 0.1%
20023
 
< 0.1%
400398
 
< 0.1%
5003
 
< 0.1%
600677
 
< 0.1%
6301
 
< 0.1%
7006
 
< 0.1%
75015
 
< 0.1%
7801
 
< 0.1%
8004547
0.2%
ValueCountFrequency (%)
800001
 
< 0.1%
640002
 
< 0.1%
520001
 
< 0.1%
480001
 
< 0.1%
440001
 
< 0.1%
4000017
< 0.1%
384001
 
< 0.1%
360003
 
< 0.1%
352001
 
< 0.1%
344001
 
< 0.1%

current_cp_credit_limit
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct106
Distinct (%)0.1%
Missing2354649
Missing (%)96.7%
Infinite0
Infinite (%)0.0%
Mean5503.516842
Minimum1000
Maximum200000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size18.6 MiB
2023-09-11T15:45:38.998501image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1000
5-th percentile2000
Q13000
median5000
Q36000
95-th percentile12000
Maximum200000
Range199000
Interquartile range (IQR)3000

Descriptive statistics

Standard deviation4152.811597
Coefficient of variation (CV)0.7545741598
Kurtosis96.90895572
Mean5503.516842
Median Absolute Deviation (MAD)1500
Skewness5.268885842
Sum441459100
Variance17245844.16
MonotonicityNot monotonic
2023-09-11T15:45:39.200080image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
500016781
 
0.7%
300014235
 
0.6%
400011210
 
0.5%
20008445
 
0.3%
60008066
 
0.3%
80004391
 
0.2%
100003258
 
0.1%
200002461
 
0.1%
70002262
 
0.1%
35001438
 
0.1%
Other values (96)7667
 
0.3%
(Missing)2354649
96.7%
ValueCountFrequency (%)
100011
 
< 0.1%
15001
 
< 0.1%
20008445
0.3%
21002
 
< 0.1%
22007
 
< 0.1%
23007
 
< 0.1%
24001
 
< 0.1%
2500740
 
< 0.1%
260010
 
< 0.1%
27006
 
< 0.1%
ValueCountFrequency (%)
2000001
 
< 0.1%
1300001
 
< 0.1%
1000001
 
< 0.1%
800005
 
< 0.1%
600007
 
< 0.1%
550001
 
< 0.1%
5000036
< 0.1%
480001
 
< 0.1%
450006
 
< 0.1%
440001
 
< 0.1%

initial_ca_credit_limit
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct231
Distinct (%)0.3%
Missing2361902
Missing (%)97.0%
Infinite0
Infinite (%)0.0%
Mean2192.955415
Minimum0
Maximum80000
Zeros3445
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size18.6 MiB
2023-09-11T15:45:39.411475image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile400
Q11200
median1600
Q32400
95-th percentile6000
Maximum80000
Range80000
Interquartile range (IQR)1200

Descriptive statistics

Standard deviation2041.100823
Coefficient of variation (CV)0.9307534526
Kurtosis56.86861066
Mean2192.955415
Median Absolute Deviation (MAD)800
Skewness4.235834927
Sum160000220
Variance4166092.572
MonotonicityNot monotonic
2023-09-11T15:45:39.608782image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
120010288
 
0.4%
16006031
 
0.2%
24006024
 
0.2%
15005888
 
0.2%
8005523
 
0.2%
20005405
 
0.2%
18003893
 
0.2%
4003818
 
0.2%
03445
 
0.1%
30002465
 
0.1%
Other values (221)20181
 
0.8%
(Missing)2361902
97.0%
ValueCountFrequency (%)
03445
0.1%
20011
 
< 0.1%
4003818
0.2%
5001
 
< 0.1%
6001104
 
< 0.1%
6301
 
< 0.1%
7008
 
< 0.1%
75014
 
< 0.1%
7801
 
< 0.1%
8005523
0.2%
ValueCountFrequency (%)
800001
< 0.1%
520001
< 0.1%
500001
< 0.1%
384001
< 0.1%
360001
< 0.1%
327001
< 0.1%
320002
< 0.1%
300002
< 0.1%
296001
< 0.1%
276001
< 0.1%

initial_cp_credit_limit
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct103
Distinct (%)0.1%
Missing2361902
Missing (%)97.0%
Infinite0
Infinite (%)0.0%
Mean4975.475939
Minimum0
Maximum200000
Zeros3155
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size18.6 MiB
2023-09-11T15:45:39.815651image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2000
Q13000
median4000
Q36000
95-th percentile12000
Maximum200000
Range200000
Interquartile range (IQR)3000

Descriptive statistics

Standard deviation4078.600499
Coefficient of variation (CV)0.8197407744
Kurtosis106.2097189
Mean4975.475939
Median Absolute Deviation (MAD)1000
Skewness5.216763843
Sum363015700
Variance16634982.03
MonotonicityNot monotonic
2023-09-11T15:45:40.016140image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
500014472
 
0.6%
300012501
 
0.5%
200011411
 
0.5%
40009343
 
0.4%
60006768
 
0.3%
80003436
 
0.1%
03155
 
0.1%
100002541
 
0.1%
200001884
 
0.1%
70001617
 
0.1%
Other values (93)5833
 
0.2%
(Missing)2361902
97.0%
ValueCountFrequency (%)
03155
 
0.1%
10007
 
< 0.1%
200011411
0.5%
21003
 
< 0.1%
22006
 
< 0.1%
23007
 
< 0.1%
24001
 
< 0.1%
2500567
 
< 0.1%
260010
 
< 0.1%
27005
 
< 0.1%
ValueCountFrequency (%)
2000001
 
< 0.1%
1300001
 
< 0.1%
800004
 
< 0.1%
600003
 
< 0.1%
520001
 
< 0.1%
5000029
< 0.1%
480001
 
< 0.1%
450004
 
< 0.1%
440001
 
< 0.1%
430001
 
< 0.1%

own_multiple_credit_card
Boolean

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
False
2429584 
True
 
5279
ValueCountFrequency (%)
False2429584
99.8%
True5279
 
0.2%
2023-09-11T15:45:40.156720image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

supplementary_credit_card
Boolean

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
False
2434716 
True
 
147
ValueCountFrequency (%)
False2434716
> 99.9%
True147
 
< 0.1%
2023-09-11T15:45:40.210065image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

credit_card_branch_location
Categorical

HIGH CARDINALITY

Distinct120
Distinct (%)< 0.1%
Missing6757
Missing (%)0.3%
Memory size18.6 MiB
-
2354649 
Aeon Credit Services Headquarters
 
26086
Klang
 
1294
Puchong
 
1285
Rawang
 
1281
Other values (115)
 
43511

Length

Max length33
Median length1
Mean length1.526003807
Min length1

Characters and Unicode

Total characters3705299
Distinct characters54
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st row-
2nd row-
3rd row-
4th row-
5th row-

Common Values

ValueCountFrequency (%)
-2354649
96.7%
Aeon Credit Services Headquarters26086
 
1.1%
Klang1294
 
0.1%
Puchong1285
 
0.1%
Rawang1281
 
0.1%
Midvalley1264
 
0.1%
Tebrau City1188
 
< 0.1%
Wangsa Maju1183
 
< 0.1%
Johor Bahru1086
 
< 0.1%
AU21083
 
< 0.1%
Other values (110)37707
 
1.5%
(Missing)6757
 
0.3%

Length

2023-09-11T15:45:40.536399image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2354649
92.5%
aeon31622
 
1.2%
credit26086
 
1.0%
services26086
 
1.0%
headquarters26086
 
1.0%
co2951
 
0.1%
bukit2589
 
0.1%
alam2333
 
0.1%
shah2333
 
0.1%
kota1969
 
0.1%
Other values (126)69797
 
2.7%

Most occurring characters

ValueCountFrequency (%)
-2354649
63.5%
e173301
 
4.7%
a125721
 
3.4%
r119839
 
3.2%
118395
 
3.2%
i71290
 
1.9%
t67742
 
1.8%
n58517
 
1.6%
d58002
 
1.6%
s54852
 
1.5%
Other values (44)502991
 
13.6%

Most occurring categories

ValueCountFrequency (%)
Dash Punctuation2354649
63.5%
Lowercase Letter982243
26.5%
Uppercase Letter243807
 
6.6%
Space Separator118395
 
3.2%
Decimal Number6205
 
0.2%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A42326
17.4%
S39160
16.1%
C32088
13.2%
H26828
11.0%
B11447
 
4.7%
K10631
 
4.4%
O10561
 
4.3%
M9735
 
4.0%
E8976
 
3.7%
N8133
 
3.3%
Other values (14)43922
18.0%
Lowercase Letter
ValueCountFrequency (%)
e173301
17.6%
a125721
12.8%
r119839
12.2%
i71290
7.3%
t67742
 
6.9%
n58517
 
6.0%
d58002
 
5.9%
s54852
 
5.6%
u46478
 
4.7%
o37377
 
3.8%
Other values (13)169124
17.2%
Decimal Number
ValueCountFrequency (%)
22410
38.8%
11614
26.0%
31415
22.8%
8765
 
12.3%
91
 
< 0.1%
Dash Punctuation
ValueCountFrequency (%)
-2354649
100.0%
Space Separator
ValueCountFrequency (%)
118395
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common2479249
66.9%
Latin1226050
33.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e173301
14.1%
a125721
 
10.3%
r119839
 
9.8%
i71290
 
5.8%
t67742
 
5.5%
n58517
 
4.8%
d58002
 
4.7%
s54852
 
4.5%
u46478
 
3.8%
A42326
 
3.5%
Other values (37)407982
33.3%
Common
ValueCountFrequency (%)
-2354649
95.0%
118395
 
4.8%
22410
 
0.1%
11614
 
0.1%
31415
 
0.1%
8765
 
< 0.1%
91
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII3705299
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
-2354649
63.5%
e173301
 
4.7%
a125721
 
3.4%
r119839
 
3.2%
118395
 
3.2%
i71290
 
1.9%
t67742
 
1.8%
n58517
 
1.6%
d58002
 
1.6%
s54852
 
1.5%
Other values (44)502991
 
13.6%

customer_with_active_prepaid_card
Boolean

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
False
2095391 
True
339472 
ValueCountFrequency (%)
False2095391
86.1%
True339472
 
13.9%
2023-09-11T15:45:40.650392image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

current_prepaid_card_balance
Real number (ℝ≥0)

MISSING
SKEWED
ZEROS

Distinct9025
Distinct (%)2.7%
Missing2095391
Missing (%)86.1%
Infinite0
Infinite (%)0.0%
Mean14.99085601
Minimum0
Maximum10000
Zeros194365
Zeros (%)8.0%
Negative0
Negative (%)0.0%
Memory size18.6 MiB
2023-09-11T15:45:40.774284image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q34
95-th percentile40.88
Maximum10000
Range10000
Interquartile range (IQR)4

Descriptive statistics

Standard deviation191.7003864
Coefficient of variation (CV)12.78782121
Kurtosis1378.993676
Mean14.99085601
Median Absolute Deviation (MAD)0
Skewness34.14174152
Sum5088975.87
Variance36749.03814
MonotonicityNot monotonic
2023-09-11T15:45:40.973149image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0194365
 
8.0%
117442
 
0.7%
212058
 
0.5%
38641
 
0.4%
207857
 
0.3%
57647
 
0.3%
45963
 
0.2%
63961
 
0.2%
83159
 
0.1%
72739
 
0.1%
Other values (9015)75640
 
3.1%
(Missing)2095391
86.1%
ValueCountFrequency (%)
0194365
8.0%
0.0157
 
< 0.1%
0.0240
 
< 0.1%
0.0332
 
< 0.1%
0.0432
 
< 0.1%
0.05493
 
< 0.1%
0.0625
 
< 0.1%
0.0722
 
< 0.1%
0.0834
 
< 0.1%
0.0927
 
< 0.1%
ValueCountFrequency (%)
100006
< 0.1%
99883
< 0.1%
9956.921
 
< 0.1%
9954.81
 
< 0.1%
9915.111
 
< 0.1%
9814.53
< 0.1%
9802.54
< 0.1%
9790.51
 
< 0.1%
9766.86
< 0.1%
9754.82
 
< 0.1%

date_of_last_top_up_of_prepaid_card
Real number (ℝ≥0)

MISSING

Distinct1157
Distinct (%)0.6%
Missing2251140
Missing (%)92.5%
Infinite0
Infinite (%)0.0%
Mean20208466.61
Minimum20170610
Maximum20220731
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size18.6 MiB
2023-09-11T15:45:41.173835image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum20170610
5-th percentile20191101
Q120200803
median20210306
Q320220216
95-th percentile20220701
Maximum20220731
Range50121
Interquartile range (IQR)19413

Descriptive statistics

Standard deviation9473.451595
Coefficient of variation (CV)0.0004687862655
Kurtosis-1.176599336
Mean20208466.61
Median Absolute Deviation (MAD)9703
Skewness-0.07176909682
Sum3.712760112 × 1012
Variance89746285.13
MonotonicityNot monotonic
2023-09-11T15:45:41.382171image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2020090214277
 
0.6%
202008037291
 
0.3%
202202167007
 
0.3%
202007064508
 
0.2%
202006034261
 
0.2%
202002034240
 
0.2%
202004023656
 
0.2%
202003023654
 
0.2%
202001023363
 
0.1%
201911013007
 
0.1%
Other values (1147)128459
 
5.3%
(Missing)2251140
92.5%
ValueCountFrequency (%)
201706101
 
< 0.1%
201906011380
0.1%
2019060266
 
< 0.1%
2019060327
 
< 0.1%
2019060419
 
< 0.1%
201906063
 
< 0.1%
2019060710
 
< 0.1%
201906088
 
< 0.1%
201906094
 
< 0.1%
2019061016
 
< 0.1%
ValueCountFrequency (%)
20220731569
< 0.1%
20220730539
< 0.1%
20220729513
< 0.1%
20220728452
< 0.1%
20220727435
< 0.1%
20220726386
< 0.1%
20220725431
< 0.1%
20220724325
< 0.1%
20220723300
< 0.1%
20220722255
< 0.1%

supplementary_prepaid_card
Boolean

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
False
2434863 
ValueCountFrequency (%)
False2434863
100.0%
2023-09-11T15:45:41.515317image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

own_multiple_prepaid_card
Boolean

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
False
2426736 
True
 
8127
ValueCountFrequency (%)
False2426736
99.7%
True8127
 
0.3%
2023-09-11T15:45:41.560403image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

prepaid_card_branch_loaction
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct75
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.847737635
Minimum-1
Maximum73
Zeros2095391
Zeros (%)86.1%
Negative2
Negative (%)< 0.1%
Memory size18.6 MiB
2023-09-11T15:45:41.681052image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile0
Q10
median0
Q30
95-th percentile34
Maximum73
Range74
Interquartile range (IQR)0

Descriptive statistics

Standard deviation11.77507699
Coefficient of variation (CV)3.060259847
Kurtosis10.92856175
Mean3.847737635
Median Absolute Deviation (MAD)0
Skewness3.379696438
Sum9368714
Variance138.652438
MonotonicityNot monotonic
2023-09-11T15:45:41.891143image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
02095391
86.1%
1413917
 
0.6%
411474
 
0.5%
910421
 
0.4%
279887
 
0.4%
289756
 
0.4%
109720
 
0.4%
189306
 
0.4%
19002
 
0.4%
158668
 
0.4%
Other values (65)247321
 
10.2%
ValueCountFrequency (%)
-12
 
< 0.1%
02095391
86.1%
19002
 
0.4%
26364
 
0.3%
36662
 
0.3%
411474
 
0.5%
54590
 
0.2%
68639
 
0.4%
78102
 
0.3%
85493
 
0.2%
ValueCountFrequency (%)
733
 
< 0.1%
729
 
< 0.1%
718
 
< 0.1%
7043
 
< 0.1%
6993
 
< 0.1%
681135
 
< 0.1%
67267
 
< 0.1%
663711
0.2%
651033
 
< 0.1%
641987
0.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
True
1342147 
False
1092716 
ValueCountFrequency (%)
True1342147
55.1%
False1092716
44.9%
2023-09-11T15:45:42.035862image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

current_loan_balance
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
ZEROS

Distinct507393
Distinct (%)37.8%
Missing1092716
Missing (%)44.9%
Infinite0
Infinite (%)0.0%
Mean8944.559685
Minimum0
Maximum664245
Zeros46476
Zeros (%)1.9%
Negative0
Negative (%)0.0%
Memory size18.6 MiB
2023-09-11T15:45:42.158269image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile21.87
Q12020
median5398.45
Q310958.87
95-th percentile32677.47
Maximum664245
Range664245
Interquartile range (IQR)8938.87

Descriptive statistics

Standard deviation11388.28855
Coefficient of variation (CV)1.273208402
Kurtosis28.842318
Mean8944.559685
Median Absolute Deviation (MAD)3986.8
Skewness3.233732131
Sum1.200491395 × 1010
Variance129693116
MonotonicityNot monotonic
2023-09-11T15:45:42.348032image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
046476
 
1.9%
0.11508
 
0.1%
0.06882
 
< 0.1%
0.04788
 
< 0.1%
0.05749
 
< 0.1%
0.08712
 
< 0.1%
0.07693
 
< 0.1%
0.03644
 
< 0.1%
0.09636
 
< 0.1%
9748567
 
< 0.1%
Other values (507383)1288492
52.9%
(Missing)1092716
44.9%
ValueCountFrequency (%)
046476
1.9%
0.01451
 
< 0.1%
0.02487
 
< 0.1%
0.03644
 
< 0.1%
0.04788
 
< 0.1%
0.05749
 
< 0.1%
0.06882
 
< 0.1%
0.07693
 
< 0.1%
0.08712
 
< 0.1%
0.09636
 
< 0.1%
ValueCountFrequency (%)
6642451
< 0.1%
5267701
< 0.1%
4961281
< 0.1%
4505151
< 0.1%
2990041
< 0.1%
2969801
< 0.1%
2419021
< 0.1%
2334761
< 0.1%
2310661
< 0.1%
2165471
< 0.1%

sanctioned_amount
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct23020
Distinct (%)1.7%
Missing1092716
Missing (%)44.9%
Infinite0
Infinite (%)0.0%
Mean10208.29011
Minimum100
Maximum1608700
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size18.6 MiB
2023-09-11T15:45:42.547596image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum100
5-th percentile1359
Q14376.5
median7660
Q311000
95-th percentile30000
Maximum1608700
Range1608600
Interquartile range (IQR)6623.5

Descriptive statistics

Standard deviation9762.452742
Coefficient of variation (CV)0.9563259507
Kurtosis707.0308611
Mean10208.29011
Median Absolute Deviation (MAD)3340
Skewness7.847171056
Sum1.370102594 × 1010
Variance95305483.55
MonotonicityNot monotonic
2023-09-11T15:45:42.745934image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1000051904
 
2.1%
2000032597
 
1.3%
1500026872
 
1.1%
500025924
 
1.1%
3000014615
 
0.6%
960014567
 
0.6%
300014168
 
0.6%
2500013906
 
0.6%
1100013847
 
0.6%
1007013700
 
0.6%
Other values (23010)1120047
46.0%
(Missing)1092716
44.9%
ValueCountFrequency (%)
1002
< 0.1%
1022
< 0.1%
1031
< 0.1%
1051
< 0.1%
1061
< 0.1%
1082
< 0.1%
1092
< 0.1%
1102
< 0.1%
1121
< 0.1%
1131
< 0.1%
ValueCountFrequency (%)
16087001
< 0.1%
9900001
< 0.1%
7729521
< 0.1%
6200001
< 0.1%
6000002
< 0.1%
5700001
< 0.1%
5140001
< 0.1%
5006701
< 0.1%
4580001
< 0.1%
3479001
< 0.1%

own_multiple_financing_products
Boolean

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
False
2243449 
True
 
191414
ValueCountFrequency (%)
False2243449
92.1%
True191414
 
7.9%
2023-09-11T15:45:42.886241image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Distinct2147
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean39.96484525
Minimum-1
Maximum2145
Zeros1126
Zeros (%)< 0.1%
Negative74814
Negative (%)3.1%
Memory size18.6 MiB
2023-09-11T15:45:43.009858image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile1
Q11
median3
Q33
95-th percentile244
Maximum2145
Range2146
Interquartile range (IQR)2

Descriptive statistics

Standard deviation127.8636712
Coefficient of variation (CV)3.199403635
Kurtosis50.37872435
Mean39.96484525
Median Absolute Deviation (MAD)2
Skewness6.05371309
Sum97308923
Variance16349.11841
MonotonicityNot monotonic
2023-09-11T15:45:43.199643image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
31092716
44.9%
1722047
29.7%
-174814
 
3.1%
2730415
 
1.2%
269902
 
0.4%
59721
 
0.4%
168745
 
0.4%
338231
 
0.3%
78197
 
0.3%
486373
 
0.3%
Other values (2137)463702
19.0%
ValueCountFrequency (%)
-174814
 
3.1%
01126
 
< 0.1%
1722047
29.7%
2470
 
< 0.1%
31092716
44.9%
4357
 
< 0.1%
59721
 
0.4%
6875
 
< 0.1%
78197
 
0.3%
83773
 
0.2%
ValueCountFrequency (%)
21451
< 0.1%
21441
< 0.1%
21431
< 0.1%
21421
< 0.1%
21411
< 0.1%
21401
< 0.1%
21391
< 0.1%
21381
< 0.1%
21371
< 0.1%
21361
< 0.1%

default
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size18.6 MiB
0
1769686 
1
665177 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2434863
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
01769686
72.7%
1665177
 
27.3%

Length

2023-09-11T15:45:43.512112image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2023-09-11T15:45:43.605387image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
01769686
72.7%
1665177
 
27.3%

Most occurring characters

ValueCountFrequency (%)
01769686
72.7%
1665177
 
27.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2434863
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
01769686
72.7%
1665177
 
27.3%

Most occurring scripts

ValueCountFrequency (%)
Common2434863
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
01769686
72.7%
1665177
 
27.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII2434863
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
01769686
72.7%
1665177
 
27.3%

Interactions

2023-09-11T15:43:40.334912image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-09-11T15:43:41.062404image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-09-11T15:43:41.720163image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-09-11T15:43:42.393703image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-09-11T15:43:42.600461image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-09-11T15:43:42.819791image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-09-11T15:43:43.038888image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-09-11T15:43:43.256163image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-09-11T15:43:43.519372image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-09-11T15:43:43.750776image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-09-11T15:43:44.392769image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-09-11T15:43:44.829042image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-09-11T15:43:45.254412image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-09-11T15:43:45.895716image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-09-11T15:43:46.546747image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-09-11T15:43:47.195553image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-09-11T15:43:47.807086image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-09-11T15:43:48.462570image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-09-11T15:43:48.659472image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-09-11T15:43:48.871313image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-09-11T15:43:49.082623image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-09-11T15:43:49.293356image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-09-11T15:43:49.549885image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-09-11T15:43:49.779825image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-09-11T15:43:50.421468image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-09-11T15:43:50.859573image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-09-11T15:43:51.287801image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-09-11T15:43:51.930614image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-09-11T15:43:52.579749image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-09-11T15:43:53.235651image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-09-11T15:43:53.836004image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-09-11T15:43:54.480091image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-09-11T15:43:54.681476image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-09-11T15:43:54.910481image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-09-11T15:43:55.138589image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-09-11T15:43:55.366568image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-09-11T15:43:55.640065image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-09-11T15:43:55.875808image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-09-11T15:43:56.531403image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-09-11T15:43:56.967702image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-09-11T15:43:57.401448image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-09-11T15:43:58.045675image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-09-11T15:43:58.693460image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-09-11T15:43:59.349607image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-09-11T15:43:59.947722image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-09-11T15:44:00.591580image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-09-11T15:44:00.788426image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-09-11T15:44:01.005844image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-09-11T15:44:01.221164image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-09-11T15:44:01.446318image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-09-11T15:44:01.717424image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-09-11T15:44:01.943031image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-09-11T15:44:02.614095image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-09-11T15:44:03.092193image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-09-11T15:44:03.542991image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-09-11T15:44:04.186998image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-09-11T15:44:04.383216image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-09-11T15:44:04.604804image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-09-11T15:44:04.821115image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-09-11T15:44:05.051101image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-09-11T15:44:05.259789image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-09-11T15:44:05.492671image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-09-11T15:44:05.724736image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-09-11T15:44:05.955893image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-09-11T15:44:06.183090image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-09-11T15:44:06.389572image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-09-11T15:44:06.622137image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-09-11T15:44:06.850401image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-09-11T15:44:07.064433image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-09-11T15:44:07.290039image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-09-11T15:44:07.529082image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-09-11T15:44:07.756960image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-09-11T15:44:07.979966image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-09-11T15:44:08.215913image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-09-11T15:44:08.443332image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-09-11T15:44:08.673438image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-09-11T15:44:08.912366image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-09-11T15:44:09.151528image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-09-11T15:44:09.382326image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-09-11T15:44:09.598470image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-09-11T15:44:09.839869image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-09-11T15:44:10.067779image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-09-11T15:44:10.293083image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-09-11T15:44:10.530887image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-09-11T15:44:10.769120image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-09-11T15:44:10.995204image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-09-11T15:44:11.209790image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-09-11T15:44:11.437474image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-09-11T15:44:11.663548image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-09-11T15:44:11.897402image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-09-11T15:44:12.121036image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-09-11T15:44:12.353624image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-09-11T15:44:12.577344image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-09-11T15:44:12.770931image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-09-11T15:44:12.987731image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-09-11T15:44:13.207250image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-09-11T15:44:13.426761image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-09-11T15:44:13.658134image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-09-11T15:44:13.892978image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-09-11T15:44:14.115952image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-09-11T15:44:14.336069image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-09-11T15:44:14.573464image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-09-11T15:44:14.804159image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-09-11T15:44:15.040641image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-09-11T15:44:15.276917image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-09-11T15:44:15.503658image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-09-11T15:44:15.731589image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-09-11T15:44:15.945263image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-09-11T15:44:16.183054image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-09-11T15:44:16.406191image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-09-11T15:44:16.628363image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-09-11T15:44:16.861442image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-09-11T15:44:17.148671image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-09-11T15:44:17.409114image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-09-11T15:44:17.660784image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-09-11T15:44:17.922925image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-09-11T15:44:18.116830image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-09-11T15:44:18.320674image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-09-11T15:44:18.532040image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-09-11T15:44:18.731834image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-09-11T15:44:18.985222image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-09-11T15:44:19.216233image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-09-11T15:44:19.483176image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-09-11T15:44:19.718713image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-09-11T15:44:19.946776image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-09-11T15:44:20.224836image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-09-11T15:44:20.469768image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-09-11T15:44:20.725191image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-09-11T15:44:20.972666image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-09-11T15:44:21.234198image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-09-11T15:44:21.434441image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-09-11T15:44:21.649649image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-09-11T15:44:21.875905image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-09-11T15:44:22.087906image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-09-11T15:44:22.343104image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-09-11T15:44:22.571612image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-09-11T15:44:22.819645image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-09-11T15:44:23.045864image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-09-11T15:44:23.274129image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-09-11T15:44:23.525025image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-09-11T15:44:24.205791image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-09-11T15:44:24.920056image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-09-11T15:44:25.575686image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-09-11T15:44:26.284392image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-09-11T15:44:26.500852image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-09-11T15:44:26.732181image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-09-11T15:44:26.964018image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-09-11T15:44:27.194335image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-09-11T15:44:27.470131image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-09-11T15:44:27.712376image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-09-11T15:44:28.366174image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-09-11T15:44:28.843382image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-09-11T15:44:29.288898image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-09-11T15:44:29.967715image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-09-11T15:44:30.420844image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-09-11T15:44:30.885556image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-09-11T15:44:31.314123image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-09-11T15:44:31.779692image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-09-11T15:44:31.977075image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-09-11T15:44:32.188745image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-09-11T15:44:32.410394image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-09-11T15:44:32.628508image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-09-11T15:44:32.872487image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-09-11T15:44:33.077624image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-09-11T15:44:33.543875image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-09-11T15:44:34.011718image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-09-11T15:44:34.485723image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-09-11T15:44:35.525807image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-09-11T15:44:35.990894image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-09-11T15:44:36.462769image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-09-11T15:44:36.904804image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-09-11T15:44:37.379849image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-09-11T15:44:37.602879image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-09-11T15:44:37.829169image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-09-11T15:44:38.062464image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-09-11T15:44:38.295716image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-09-11T15:44:38.555040image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-09-11T15:44:38.793003image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-09-11T15:44:39.249303image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-09-11T15:44:39.706754image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-09-11T15:44:40.141664image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-09-11T15:44:40.605800image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-09-11T15:44:41.301171image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-09-11T15:44:41.992599image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-09-11T15:44:42.639058image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-09-11T15:44:43.317390image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-09-11T15:44:43.519642image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-09-11T15:44:43.757712image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-09-11T15:44:43.993945image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-09-11T15:44:44.228824image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-09-11T15:44:44.515309image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-09-11T15:44:44.750831image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-09-11T15:44:45.440513image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-09-11T15:44:45.905125image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-09-11T15:44:46.345028image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-09-11T15:44:46.992218image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Correlations

2023-09-11T15:45:43.725353image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2023-09-11T15:45:44.586812image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2023-09-11T15:45:44.957246image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2023-09-11T15:45:45.379887image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2023-09-11T15:45:45.948877image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2023-09-11T15:44:49.654702image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-09-11T15:44:59.563908image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-09-11T15:45:18.655720image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2023-09-11T15:45:24.263749image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

customer_iddate_of_reportgenderoccupationnationalityracemarital_statusagehome_addressresidence_typeeducational_qualificationtype_of_employmentannual_incomevip_statusnumber_of_dependentsmobile_app_statusopt_outcustomer_with_active_credit_cardcurrent_ca_credit_limitcurrent_cp_credit_limitinitial_ca_credit_limitinitial_cp_credit_limitown_multiple_credit_cardsupplementary_credit_cardcredit_card_branch_locationcustomer_with_active_prepaid_cardcurrent_prepaid_card_balancedate_of_last_top_up_of_prepaid_cardsupplementary_prepaid_cardown_multiple_prepaid_cardprepaid_card_branch_loactioncustomer_with_active_financing_productcurrent_loan_balancesanctioned_amountown_multiple_financing_productsfinancing_product_branch_locationdefault
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Last rows

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